Speech recognition and speaker verification use similar analysis tools to achieve its goals. An input utterance is first processed to determine its essential characteristics. Typically, input utterances are converted to cepstrum coefficients. A cepstrum is an inverse Fourier transform of the log power spectrum. In a training phase the cepstrum coefficients are saved to form code books for specific utterances. For instance, a code book might have codes for the numeral zero through nine. In speech recognition, an input utterance is compared to the codes (training utterances) in the code book to determine which is most similar. In speech recognition the code is a generalized representation of many people's way of forming an utterance (e.g., “zero”). In speaker verification the codes represent the individual characteristics of the speaker and the verification system tries determine if a person's code is more similar to an input utterance, than an impostor code. As a result the codes in a speaker verification system emphasis individual characteristics, while in a speech recognition system the codes generalize over many individual speakers. Speaker verification has potential applications in a number of voice activated systems, such as banking over the telephone. Unfortunately, present speaker verification systems have not proven reliable enough for these applications.
Thus there exists a need for an improved speaker verification system.